Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Vassilis Vassiliadis, Charalampos Chalios, Konstantinos Parasyris, Christos D. Antonopoulos, Spyros Lalis, Nikolaos Bellas, Hans Vandierendonck, Dimitrios S. Nikolopoulos

Research output: Contribution to journalArticle

9 Citations (Scopus)
329 Downloads (Pure)

Abstract

Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints. Evaluation on a dual-socket 16-core Intel platform using 9 benchmark kernels shows that the proposed framework picks the optimal configuration with high accuracy. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.
Original languageEnglish
Pages (from-to)1078-1098
Number of pages14
JournalInternational Journal of Parallel Programming
Volume44
Issue number5
Early online date24 Mar 2016
DOIs
Publication statusPublished - Oct 2016

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